Virtual Visual Stimulation
Virtual Visual Stimulation - Related Videos
09:23
Integrating Automated Simulation Workflows with 3D Visualization for Virtual Experiments in the Metaverse
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2026
A generalized, FAIR-compliant method is presented for domain-expert researchers seeking to integrate simulation and data-processing tools into automated workflows for 3D virtual experiments. A neutronics example demonstrates setting up a local Galaxy instance, wrapping OpenMC and file-conversion tools, launching workflows from Omniverse, and visualizing the converted 3D outputs.
08:09
Multifunctional Setup for Studying Human Motor Control Using Transcranial Magnetic Stimulation, Electromyography, Motion Capture, and Virtual Reality
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Cited by 13 •
2015
Transcranial magnetic stimulation, electromyography, and 3D motion capture are commonly used non-invasive techniques for investigating neuromuscular function in humans. In this paper, we describe a protocol that synchronously samples data generated by all three of these tools along with the unique addition of virtual reality stimulus presentation and feedback.
04:31
In Vitro Stimulation and Visualization of Macrophage Extracellular Traps
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2025
This video demonstrates in vitro stimulation of macrophage extracellular traps (METs) in human monocyte-derive macrophages using different inflammatory stimuli such as TNF-alpha, PMA, and HOCL. The video also demonstrates the method of SYTOX dye staining of METs for visualization.
08:12
Two-photon Calcium Imaging in Mice Navigating a Virtual Reality Environment
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Cited by 85 •
2014
Here we describe the experimental procedures involved in two-photon imaging of mouse cortex during behavior in a virtual reality environment.
10:38
Human Fear Conditioning Conducted in Full Immersion 3-Dimensional Virtual Reality
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Cited by 25 •
2010
Classical fear conditioning paradigm was adapted for human participants in a fully immersive virtual reality setting. Using a discrimination paradigm, conditioned fear, cue and context memory retention, and extinction was measured with skin conductance response to dynamic virtual snakes and spiders (the conditioned stimuli) in two distinct virtual contexts.
FAQs
Each visual feature can be treated as an experimental variable and changed while other scene properties remain controlled. Motion may be adjusted independently from contrast, or timing can be varied without altering spatial arrangement. Comparing neural activity and behavior across these conditions helps identify which aspects of visual information influence processing, perception, attention, or action.
Repeatable presentation allows the same visual conditions to be shown across experimental comparisons, making differences in neural activity or behavior easier to attribute to manipulated scene features. At the same time, computer-generated environments can represent realistic situations. This combination is valuable when researchers need experimental control without abandoning visually meaningful contexts such as navigation or action.
Researchers can compare the visual conditions presented with changes in neural activity and behavioral performance. Adding eye tracking provides information about visual sampling, while neurophysiological recording provides a measure of activity during stimulation. Together, these measurements help connect what a subject sees with perceptual processing, attention, spatial navigation, multisensory integration, or visually guided action.
An effective design specifies the visual features to manipulate, including motion, contrast, depth, spatial arrangement, or timing, and maintains repeatable conditions for comparison. The chosen scene should also match the research question, whether the goal concerns perception, attention, navigation, multisensory integration, or visually guided action. This alignment makes measured neural and behavioral changes interpretable.
Eye tracking, behavioral tests, and neurophysiological recording provide complementary outcomes. Eye tracking can be used alongside the visual presentation, behavioral tests indicate how performance changes, and neurophysiological recording captures associated neural activity. Using these measures together helps researchers examine relationships among visual input, brain responses, and cognitive or action-related outcomes rather than relying on a single result.
It is useful when researchers need to study visual processing under controlled yet realistic conditions. Applications described for the method include perception, attention, spatial navigation, multisensory integration, and visually guided action. By simulating relevant situations while controlling scene properties, the approach can help relate visual experience to brain mechanisms and cognitive function across these complementary areas.